





'''simple docstring'''






import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)






def   __A    (				a_    :	Optional[int]						):
		lowerCAmelCase  :     List[str]       						= VideoMAEConfig()

		set_architecture_configs(a_   ,a_						)

		if "finetuned" not in model_name:
				lowerCAmelCase  :     List[str]       						= False

		if "finetuned" in model_name:
				lowerCAmelCase  :     Optional[int]       						= "huggingface/label-files"
				if "kinetics" in model_name:
						lowerCAmelCase  :     List[str]       						= 4_0_0
						lowerCAmelCase  :     int       						= "kinetics400-id2label.json"
				elif "ssv2" in model_name:
						lowerCAmelCase  :     int       						= 1_7_4
						lowerCAmelCase  :     List[str]       						= "something-something-v2-id2label.json"
				else:
						raise ValueError("Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."						)
				lowerCAmelCase  :     Union[str, Any]       						= json.load(open(hf_hub_download(a_   ,a_   ,repo_type="dataset"						)   ,"r"						)						)
				lowerCAmelCase  :     List[Any]       						= {int(a_						): v for k, v in idalabel.items()}
				lowerCAmelCase  :     Any       						= idalabel
				lowerCAmelCase  :     List[str]       						= {v: k for k, v in idalabel.items()}

		return config






def   __A    (				a_    :	List[Any]   ,a_    :	List[str]						):
		if "small" in model_name:
				lowerCAmelCase  :     Tuple       						= 3_8_4
				lowerCAmelCase  :     Any       						= 1_5_3_6
				lowerCAmelCase  :     Optional[int]       						= 1_2
				lowerCAmelCase  :     str       						= 1_6
				lowerCAmelCase  :     List[Any]       						= 1_2
				lowerCAmelCase  :     Dict       						= 3
				lowerCAmelCase  :     List[Any]       						= 1_9_2
				lowerCAmelCase  :     Dict       						= 7_6_8
		elif "large" in model_name:
				lowerCAmelCase  :     List[Any]       						= 1_0_2_4
				lowerCAmelCase  :     Dict       						= 4_0_9_6
				lowerCAmelCase  :     Tuple       						= 2_4
				lowerCAmelCase  :     List[str]       						= 1_6
				lowerCAmelCase  :     List[Any]       						= 1_2
				lowerCAmelCase  :     str       						= 8
				lowerCAmelCase  :     Tuple       						= 5_1_2
				lowerCAmelCase  :     Optional[Any]       						= 2_0_4_8
		elif "huge" in model_name:
				lowerCAmelCase  :     List[str]       						= 1_2_8_0
				lowerCAmelCase  :     Optional[int]       						= 5_1_2_0
				lowerCAmelCase  :     Optional[Any]       						= 3_2
				lowerCAmelCase  :     List[str]       						= 1_6
				lowerCAmelCase  :     str       						= 1_2
				lowerCAmelCase  :     Tuple       						= 8
				lowerCAmelCase  :     Optional[Any]       						= 6_4_0
				lowerCAmelCase  :     str       						= 2_5_6_0
		elif "base" not in model_name:
				raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""						)






def   __A    (				a_    :	List[Any]						):
		if "encoder." in name:
				lowerCAmelCase  :     Union[str, Any]       						= name.replace("encoder."   ,""						)
		if "cls_token" in name:
				lowerCAmelCase  :     Optional[Any]       						= name.replace("cls_token"   ,"videomae.embeddings.cls_token"						)
		if "decoder_pos_embed" in name:
				lowerCAmelCase  :     int       						= name.replace("decoder_pos_embed"   ,"decoder.decoder_pos_embed"						)
		if "pos_embed" in name and "decoder" not in name:
				lowerCAmelCase  :     Optional[int]       						= name.replace("pos_embed"   ,"videomae.embeddings.position_embeddings"						)
		if "patch_embed.proj" in name:
				lowerCAmelCase  :     List[str]       						= name.replace("patch_embed.proj"   ,"videomae.embeddings.patch_embeddings.projection"						)
		if "patch_embed.norm" in name:
				lowerCAmelCase  :     Optional[Any]       						= name.replace("patch_embed.norm"   ,"videomae.embeddings.norm"						)
		if "decoder.blocks" in name:
				lowerCAmelCase  :     Optional[Any]       						= name.replace("decoder.blocks"   ,"decoder.decoder_layers"						)
		if "blocks" in name:
				lowerCAmelCase  :     Tuple       						= name.replace("blocks"   ,"videomae.encoder.layer"						)
		if "attn.proj" in name:
				lowerCAmelCase  :     Any       						= name.replace("attn.proj"   ,"attention.output.dense"						)
		if "attn" in name and "bias" not in name:
				lowerCAmelCase  :     Union[str, Any]       						= name.replace("attn"   ,"attention.self"						)
		if "attn" in name:
				lowerCAmelCase  :     Dict       						= name.replace("attn"   ,"attention.attention"						)
		if "norm1" in name:
				lowerCAmelCase  :     List[str]       						= name.replace("norm1"   ,"layernorm_before"						)
		if "norm2" in name:
				lowerCAmelCase  :     List[Any]       						= name.replace("norm2"   ,"layernorm_after"						)
		if "mlp.fc1" in name:
				lowerCAmelCase  :     Tuple       						= name.replace("mlp.fc1"   ,"intermediate.dense"						)
		if "mlp.fc2" in name:
				lowerCAmelCase  :     int       						= name.replace("mlp.fc2"   ,"output.dense"						)
		if "decoder_embed" in name:
				lowerCAmelCase  :     Union[str, Any]       						= name.replace("decoder_embed"   ,"decoder.decoder_embed"						)
		if "decoder_norm" in name:
				lowerCAmelCase  :     int       						= name.replace("decoder_norm"   ,"decoder.decoder_norm"						)
		if "decoder_pred" in name:
				lowerCAmelCase  :     Union[str, Any]       						= name.replace("decoder_pred"   ,"decoder.decoder_pred"						)
		if "norm.weight" in name and "decoder" not in name and "fc" not in name:
				lowerCAmelCase  :     Any       						= name.replace("norm.weight"   ,"videomae.layernorm.weight"						)
		if "norm.bias" in name and "decoder" not in name and "fc" not in name:
				lowerCAmelCase  :     Union[str, Any]       						= name.replace("norm.bias"   ,"videomae.layernorm.bias"						)
		if "head" in name and "decoder" not in name:
				lowerCAmelCase  :     int       						= name.replace("head"   ,"classifier"						)

		return name






def   __A    (				a_    :	Dict   ,a_    :	Tuple						):
		for key in orig_state_dict.copy().keys():
				lowerCAmelCase  :     int       						= orig_state_dict.pop(a_						)

				if key.startswith("encoder."						):
						lowerCAmelCase  :     Any       						= key.replace("encoder."   ,""						)

				if "qkv" in key:
						lowerCAmelCase  :     str       						= key.split("."						)
						if key.startswith("decoder.blocks"						):
								lowerCAmelCase  :     Any       						= config.decoder_hidden_size
								lowerCAmelCase  :     str       						= int(key_split[2]						)
								lowerCAmelCase  :     Any       						= "decoder.decoder_layers."
								if "weight" in key:
										lowerCAmelCase  :     Optional[Any]       						= val[:dim, :]
										lowerCAmelCase  :     List[Any]       						= val[dim : dim * 2, :]
										lowerCAmelCase  :     Union[str, Any]       						= val[-dim:, :]
						else:
								lowerCAmelCase  :     int       						= config.hidden_size
								lowerCAmelCase  :     Union[str, Any]       						= int(key_split[1]						)
								lowerCAmelCase  :     Dict       						= "videomae.encoder.layer."
								if "weight" in key:
										lowerCAmelCase  :     List[Any]       						= val[:dim, :]
										lowerCAmelCase  :     Union[str, Any]       						= val[dim : dim * 2, :]
										lowerCAmelCase  :     Tuple       						= val[-dim:, :]
				else:
						lowerCAmelCase  :     Dict       						= val

		return orig_state_dict






def   __A    (				):
		lowerCAmelCase  :     Optional[Any]       						= hf_hub_download(
		    repo_id="hf-internal-testing/spaghetti-video"   ,filename="eating_spaghetti.npy"   ,repo_type="dataset"						)
		lowerCAmelCase  :     int       						= np.load(a_						)
		return list(a_						)






def   __A    (				a_    :	List[Any]   ,a_    :	Union[str, Any]   ,a_    :	Any   ,a_    :	Tuple						):
		lowerCAmelCase  :     str       						= get_videomae_config(a_						)

		if "finetuned" in model_name:
				lowerCAmelCase  :     Union[str, Any]       						= VideoMAEForVideoClassification(a_						)
		else:
				lowerCAmelCase  :     Optional[int]       						= VideoMAEForPreTraining(a_						)

		# download original checkpoint, hosted on Google Drive
		lowerCAmelCase  :     Optional[int]       						= "pytorch_model.bin"
		gdown.cached_download(a_   ,a_   ,quiet=a_						)
		lowerCAmelCase  :     int       						= torch.load(a_   ,map_location="cpu"						)
		if "model" in files:
				lowerCAmelCase  :     Dict       						= files["model"]
		else:
				lowerCAmelCase  :     int       						= files["module"]
		lowerCAmelCase  :     Dict       						= convert_state_dict(a_   ,a_						)

		model.load_state_dict(a_						)
		model.eval()

		# verify model on basic input
		lowerCAmelCase  :     int       						= VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   ,image_std=[0.5, 0.5, 0.5]						)
		lowerCAmelCase  :     Union[str, Any]       						= prepare_video()
		lowerCAmelCase  :     Tuple       						= image_processor(a_   ,return_tensors="pt"						)

		if "finetuned" not in model_name:
				lowerCAmelCase  :     List[Any]       						= hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"   ,filename="bool_masked_pos.pt"						)
				lowerCAmelCase  :     List[str]       						= torch.load(a_						)

		lowerCAmelCase  :     str       						= model(**a_						)
		lowerCAmelCase  :     Dict       						= outputs.logits

		lowerCAmelCase  :     str       						= [
		    "videomae-small-finetuned-kinetics",
		    "videomae-small-finetuned-ssv2",
		    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
		    "videomae-base-short",
		    "videomae-base-short-finetuned-kinetics",
		    "videomae-base",
		    "videomae-base-finetuned-kinetics",
		    "videomae-large",
		    "videomae-large-finetuned-kinetics",
		    "videomae-huge-finetuned-kinetics",
		    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
		    "videomae-base-short-ssv2",
		    "videomae-base-short-finetuned-ssv2",
		    "videomae-base-ssv2",
		    "videomae-base-finetuned-ssv2",
		]

		# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
		if model_name == "videomae-small-finetuned-kinetics":
				lowerCAmelCase  :     List[str]       						= torch.Size([1, 4_0_0]						)
				lowerCAmelCase  :     List[Any]       						= torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]						)
		elif model_name == "videomae-small-finetuned-ssv2":
				lowerCAmelCase  :     Tuple       						= torch.Size([1, 1_7_4]						)
				lowerCAmelCase  :     int       						= torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]						)
		elif model_name == "videomae-base":
				lowerCAmelCase  :     str       						= torch.Size([1, 1_4_0_8, 1_5_3_6]						)
				lowerCAmelCase  :     Union[str, Any]       						= torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]						)
		elif model_name == "videomae-base-short":
				lowerCAmelCase  :     Dict       						= torch.Size([1, 1_4_0_8, 1_5_3_6]						)
				lowerCAmelCase  :     Tuple       						= torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]						)
				# we verified the loss both for normalized and unnormalized targets for this one
				lowerCAmelCase  :     Optional[Any]       						= torch.tensor([0.5_1_4_2]						) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]						)
		elif model_name == "videomae-large":
				lowerCAmelCase  :     Any       						= torch.Size([1, 1_4_0_8, 1_5_3_6]						)
				lowerCAmelCase  :     List[Any]       						= torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]						)
		elif model_name == "videomae-large-finetuned-kinetics":
				lowerCAmelCase  :     int       						= torch.Size([1, 4_0_0]						)
				lowerCAmelCase  :     str       						= torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]						)
		elif model_name == "videomae-huge-finetuned-kinetics":
				lowerCAmelCase  :     Optional[int]       						= torch.Size([1, 4_0_0]						)
				lowerCAmelCase  :     Optional[int]       						= torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]						)
		elif model_name == "videomae-base-short-finetuned-kinetics":
				lowerCAmelCase  :     Optional[int]       						= torch.Size([1, 4_0_0]						)
				lowerCAmelCase  :     Tuple       						= torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]						)
		elif model_name == "videomae-base-finetuned-kinetics":
				lowerCAmelCase  :     Union[str, Any]       						= torch.Size([1, 4_0_0]						)
				lowerCAmelCase  :     List[Any]       						= torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]						)
		elif model_name == "videomae-base-short-ssv2":
				lowerCAmelCase  :     Union[str, Any]       						= torch.Size([1, 1_4_0_8, 1_5_3_6]						)
				lowerCAmelCase  :     str       						= torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]						)
		elif model_name == "videomae-base-short-finetuned-ssv2":
				lowerCAmelCase  :     Optional[Any]       						= torch.Size([1, 1_7_4]						)
				lowerCAmelCase  :     List[str]       						= torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]						)
		elif model_name == "videomae-base-ssv2":
				lowerCAmelCase  :     List[str]       						= torch.Size([1, 1_4_0_8, 1_5_3_6]						)
				lowerCAmelCase  :     Any       						= torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]						)
		elif model_name == "videomae-base-finetuned-ssv2":
				lowerCAmelCase  :     int       						= torch.Size([1, 1_7_4]						)
				lowerCAmelCase  :     List[str]       						= torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]						)
		else:
				raise ValueError(f'''Model name not supported. Should be one of {model_names}'''						)

		# verify logits
		assert logits.shape == expected_shape
		if "finetuned" in model_name:
				assert torch.allclose(logits[0, :3]   ,a_   ,atol=1e-4						)
		else:
				print("Logits:"   ,logits[0, :3, :3]						)
				assert torch.allclose(logits[0, :3, :3]   ,a_   ,atol=1e-4						)
		print("Logits ok!"						)

		# verify loss, if applicable
		if model_name == "videomae-base-short":
				lowerCAmelCase  :     Any       						= outputs.loss
				assert torch.allclose(a_   ,a_   ,atol=1e-4						)
				print("Loss ok!"						)

		if pytorch_dump_folder_path is not None:
				print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''						)
				image_processor.save_pretrained(a_						)
				model.save_pretrained(a_						)

		if push_to_hub:
				print("Pushing to the hub..."						)
				model.push_to_hub(a_   ,organization="nielsr"						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
					    type=str,
					    help=(
					        """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
					        """ download link."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
					    type=str,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)

					lowerCAmelCase									=   parser.parse_args()
					convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)










'''simple docstring'''






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def   __A    (				):
		lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= 9, 1_4  # noqa: F841
		lowerCAmelCase  :     int       						= [
		    [0, 1, 4],
		    [0, 7, 8],
		    [1, 2, 8],
		    [7, 8, 7],
		    [7, 6, 1],
		    [2, 8, 2],
		    [8, 6, 6],
		    [2, 3, 7],
		    [2, 5, 4],
		    [6, 5, 2],
		    [3, 5, 1_4],
		    [3, 4, 9],
		    [5, 4, 1_0],
		    [1, 7, 1_1],
		]

		lowerCAmelCase  :     Optional[Any]       						= defaultdict(a_						)
		for nodea, nodea, cost in edges:
				adjancency[nodea].append([nodea, cost]						)
				adjancency[nodea].append([nodea, cost]						)

		lowerCAmelCase  :     int       						= mst(a_						)

		lowerCAmelCase  :     Tuple       						= [
		    [7, 6, 1],
		    [2, 8, 2],
		    [6, 5, 2],
		    [0, 1, 4],
		    [2, 5, 4],
		    [2, 3, 7],
		    [0, 7, 8],
		    [3, 4, 9],
		]

		for answer in expected:
				lowerCAmelCase  :     List[str]       						= tuple(answer[:2]						)
				lowerCAmelCase  :     int       						= tuple(edge[::-1]						)
				assert edge in result or reverse in result



